The present invention relates to a digital communication application, and particularly, to the linear successive user allocation in a multi-cell MIMO (MIMO=multiple inputs multiple outputs) environment.
In a downlink of a cellular network information is transmitted with multiple transmit antennas to users equipped with multiple receive antennas (MIMO). Multi-carrier (OFDM) transmission can also be employed. In the downlink of a cellular system, inter-cell interference (ICI) can be a severely limiting factor, especially users at the cell edge are affected and might be excluded from network service.
A possible solution to completely eliminate ICI is the joint encoding of information over multiple base stations (G. J. Foschini, K. Karakayali, and R. A. Valenzuela. Coordinating multiple antenna cellular networks to achieve enormous spectral efficiency. Communications, IEE Proceedings-, 153(4):548-555, August 2006, S. Shamai and B. M. Zaidel. Enhancing the cellular downlink capacity via co-processing at the transmitting end. Vehicular Technology Conference, 2001. VTC 2001 Spring. IEEE VTS 53rd, 3:1745-1749 vol. 3, 2001), so-called network MIMO. Joint encoding over geographically distributed antennas renders the network into a super-cell, which is related to the MIMO broadcast scenario (S. Vishwanathan, N. Jindal, and A. Goldsmith. Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels. IEEE Trans. Inf. Theory, 49(10):2658-2668, October 2003, H. Weingarten, Y. Steinberg, and S. Shamai. The capacity region of the Gaussian multiple-input multiple-output broadcast channel. IEEE Trans. Inf. Theory, 52(9):3936-3964, September 2006). In case full channel state information (CSI) and all data is available at a central controller, network MIMO can efficiently exploit all spatial degrees of freedom to eliminate ICI. Although the network's performance is no longer limited by interference, there is a huge amount of additional complexity compared to single cell signal processing. Additionally, network MIMO involves a backhaul with high capacity and low delay in order to exchange the CSI of all users. Furthermore, joint encoding relies on coherent transmission of all antenna arrays, which necessitates perfect synchronization in the network that might be difficult to implement in practice. Therefore, methods aiming at elimination of interference by cooperation of the base stations, while every user is served by a single base station, are attractive for deployable networks.
In order to cancel interference, user signals are orthogonalized in the available signal space constituted by the available resources, for example time, frequency, and space. A simple scheme that completely removes ICI is to exclusively allocate carriers to base stations and apply any single cell algorithm on the allocated carriers, which corresponds to the classical frequency reuse planning, a very simple form of interference management. Besides the poor spectral efficiency, frequency reuse partitioning leaves out the opportunity for cooperation in the spatial domain. The availability of multiple antennas at transmitter and receiver allows to serve multiple users interference free at the same time on the same frequency by spatial multiplexing. Interference coordination by adjusting the transmission space of each user is well understood and can be solved optimally for a single cell (S. Vishwanathan, N. Jindal, and A. Goldsmith. Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels. IEEE Trans. Inf. Theory, 49(10):2658-2668, October 2003, H. Weingarten, Y. Steinberg, and S. Shamai. The capacity region of the Gaussian multiple-input multiple-output broadcast channel. IEEE Trans. Inf. Theory, 52(9):3936-3964, September 2006). In conventional cellular network design, signal processing in the spatial domain is only performed per cell, but interesting research towards extending spatial multiplexing over multiple base stations is emerging. There is a huge variety of work concerning interference management in cellular systems where the base stations have multiple antennas and the receivers are equipped with a single antenna (MISO). First steps into coordinating the transmission spaces used by each cell are proposals that perform a joint decision on the users to schedule (Wan Choi and J. G. Andrews. The capacity gain from intercell scheduling in multi-antenna systems. IEEE Trans. Wireless Commun., 7(2):714-725, February 2008, Suman Das, Harish Viswanathan, and G. Rittenhouse. Dynamic load balancing through coordinated scheduling in packet data systems. In Proc. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2003., volume 1, pages 786-796 vol. 1, March-3 April 2003), where at each time slot only a single user per cell is active. Each user is served using a transmit filter matched to the MISO channel and by the joint scheduling decision transmit filters are combined such that interference is reduced. Clearly, it might be advantageous to select transmit filters that are not optimal for the user, but reduce the interference caused to other users (Zhang and J. G. Andrews. Adaptive Spatial Intercell Interference Cancellation in Multicell Wireless Networks. Arxiv preprint arXiv:0909.2894, 2009, H. Dahrouj and Wei Yu. Coordinated beamforming for the multi-cell multi-antenna wireless system. In Proc. 42nd Annual Conference on Information Sciences and Systems, CISS 2008., pages 429-434, March 2008). Coordinated transmission strategies for full MIMO systems are mainly available for smaller scenarios, for example two or multiple interfering point-to-point transmissions (Xiaohu Shang, Biao Chen, G. Kramer, and H. V. Poor. On the capacity of MIMO interference channels. In Communication, Control, and Computing, 2008 46th Annual Allerton Conference on, pages 700-707, September 2008, Changxin Shi, D. A. Schmidt, R. A. Berry, M. L. Honig, and W. Utschick. Distributed interference pricing for the MIMO interference channel. In Proc. IEEE International Conference on Communications, ICC 2009., pages 1-5, June 2009). For the specific scenario regarded, a network with multiple antennas employed at the transmitters and receivers, good base station cooperation schemes that use all available degrees of freedom, including the spatial domain, are not available so far.
In a successive user allocation which is performed sector by sector, the base station controller typically has available the full MIMO channel from each user to the transmission section in the sector, i.e. to the transmission antenna array having a plurality of transmission antennas. This channel information can be measured or estimated in a blind or guided channel estimation process. Now, when the base station controller is aware of the channel information between each user and the transmitter antenna array, the base station controller performs a calculation in order to find out a first user which is to be allocated to first data stream. Typically, the user having the best transmission channel is selected, but other criteria can be used as well. The best user means in this context the user having the highest data throughput or data rate. When this user is identified, the transmit parameters for the individual transmit circuits which are associated with each antenna in the transmitter antenna array are calculated. The same is performed for the receive circuits associated with the individual receiver antennas attached to the corresponding user. Based on the selected user, the channel information for all remaining users is updated as known in successive MIMO processing, and in the next allocation step, the next user is determined in this individual sector, where the next user is again the user allowing the highest data rate under the assumption that the first allocated user is not disturbed by the transmission to the second user.
Additionally, the transmit parameters for the transmitter antennas in the transmitter antenna array and the receive parameters for the receiver circuits in the receiver antenna array attached to the user are calculated. This successive procedure is performed until the determination criterion is reached, which is, for example, that all users that are in the sector are satisfied. An alternative criterion is that a minimum data rate for a certain user cannot be guaranteed anymore due to the fact that in a certain sector only a small number of MIMO channels exist. When, for example, it is considered that there are five users positioned at nearly the same geographical position, then the chances are reduced that five orthogonal MIMO channels can be found for all five users. Stated differently, the chances will be higher to find five orthogonal MIMO channels to five users, when the five users are positioned at different places in the sector. Additionally, the chances of finding enough MIMO channels within the sector are higher when the users are distributed in the sector so that the distance between the individual users and the transmitter array is not extremely different. When, for example, it is considered that one user is standing very close to the base station, a very good channel can be expected, but this very good channel might have significant negative impact for the other users in the cell in that only quite bad spatial channels can be calculated for these other users.
In a multi-cell environment, where for example, a base station has three directional antenna arrays where each directional antenna array defines one sector, a single base station will serve three sectors, where these three sectors are adjacent to each other.
When the successive user allocation is performed for each sector individually, any interferences from one sector to the other or from one cell to the other will degrade the situation particularly for users which are positioned at cell boundaries. This, however, can be significantly different depending on the actual situation in a sector, i.e. which users have been allocated which data streams and which users have associated transmit circuit parameters in the receive and transmit antenna arrays. Anyway, potential interference will result in the situation that it would have been better to allocate a stream to a different user which is not positioned close to the boundary between sectors although the user positioned at a boundary between two sectors seems to have a better channel due to the fact that the user positioned more in the middle of the sector does not suffer from an inter-cell interference from the neighboring sector. Due to the fact, however, that the inter-cell interference affected user actually received her or his channel, but is interfered from the neighboring sector, the whole data rate situation in a current sector is clearly sub-optimum, since the base station controller performing the successive user allocation has actually ignored the fact that a sector is not completely isolated from a neighboring sector, but in fact is positioned close to the sector and that there are inter-sector dependencies.
On the other hand, a non-successive user allocation in a large network could also be performed by considering all interferences, but this procedure turns out to be extremely complicated and necessitates extremely high computation resources particularly in the situation where there are many different cells and where there are quite a number of users in each cell as is, for example, the case in urban areas where there are small cells, many users in each cell and due to the small size of the cells, a high possibility for significant amount of users positioned at cell boundaries. Furthermore, such a fully network-wide user allocation processes would necessitate an enormous effort of inter base station communication. Basically, every base station in the network would have to be connected to every other base station in the network by a low delay communication scheme so that the enormous amount of channel information from all users to all transmitters in all network sectors can be distributed over the network. Furthermore, the distribution of the transmit parameters to the transmit antenna arrays of all base stations in the network and the distribution of the receive parameters to all users in the network from the central allocation processor is also a task which necessitates significant resources for computation and distribution.